| ISSN |
2220-3206 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Geriatrics & Gerontology |
| Manuscript Type |
Randomized Clinical Trial |
| Article Title |
Community-based assisted screening for mild cognitive impairment using gait and handwriting kinematic parameters analysis
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Yin-Xia Ren, Bei Wu, Jian-Lin Lou, Xiao-Rong Zhu, Chen Zhang, Qing Lang, Zhu-Qin Wei, Li-Ming Su, Heng-Nian Qi and Li-Na Wang |
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Natural Science Foundation of China |
72174061 and 71704053 |
| Key Research and Development Program of Zhejiang Province |
2025C02106 |
| China Scholarship Council Foundation |
202308330251 |
| Health Science and Technology Project of Zhejiang Provincial Health Commission |
2022KY370 |
|
| Corresponding Author |
Li-Na Wang, PhD, Professor, School of Medicine, Huzhou Key Laboratory of Precise Prevention and Control of Major Chronic Diseases, Huzhou University, No. 759 Erhuan East Road, Huzhou 313000, Zhejiang Province, China. 02474@zjhu.edu.cn |
| Key Words |
Mild cognitive impairment; Early detection; Digital health; Gait; Handwriting |
| Core Tip |
This study introduces sensitive biomarkers for assisted screening of mild cognitive impairment (MCI) by integrating gait and handwriting kinematic parameters. The findings demonstrate that the combination of gait analysis and handwriting tasks achieves a detection accuracy of 74.44% for MCI, outperforming single-task assessments. These parameters could complement traditional tool like the Montreal cognitive assessment by reducing reliance on subjective assessments and minimizing cultural, language, and educational biases. This research establishes a foundation for translating these precision biomarkers into cost-effective, portable, and scalable wearable technologies, which could enable large-scale, rapid screening of high-risk individuals with MCI in community settings. |
| Publish Date |
2025-08-26 10:18 |
| Citation |
<p>Ren YX, Wu B, Lou JL, Zhu XR, Zhang C, Lang Q, Wei ZQ, Su LM, Qi HN, Wang LN. Community-based assisted screening for mild cognitive impairment using gait and handwriting kinematic parameters analysis. <i>World J Psychiatry</i> 2025; 15(9): 109478</p> |
| URL |
https://www.wjgnet.com/2220-3206/full/v15/i9/109478.htm |
| DOI |
https://dx.doi.org/10.5498/wjp.v15.i9.109478 |
